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   <div id="projectname">Parallel Gaussian Process Regression
   &#160;<span id="projectnumber">1.0.0</span>
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   <div id="projectbrief">The implementation of parallel Gaussian process (GP) regression is based on the following publication: Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan &amp; Patrick Jaillet. Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013), Bellevue, WA, Jul 11-15, 2013.</div>
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<div class="title">pgpr_picf_predict.h</div>  </div>
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<a href="pgpr__picf__predict_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;</div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#ifndef GP_PREDICT_H__</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor"></span><span class="preprocessor">#define GP_PREDICT_H__</span></div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="preprocessor"></span></div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="preprocessor">#include &lt;string&gt;</span></div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="preprocessor">#include &quot;psvm/matrix.h&quot;</span></div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="preprocessor">#include &quot;psvm/document.h&quot;</span></div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="preprocessor">#include &quot;psvm/gp_parm.h&quot;</span></div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="preprocessor">#include &quot;psvm/cov.h&quot;</span></div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="preprocessor">#include &lt;cstdlib&gt;</span></div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="preprocessor">#include &lt;cstdio&gt;</span></div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#include &lt;cstring&gt;</span></div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="preprocessor">#include &lt;utility&gt;</span></div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="preprocessor">#include &lt;string&gt;</span></div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &lt;vector&gt;</span></div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &quot;psvm/document.h&quot;</span></div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">#include &quot;psvm/matrix_manipulation.h&quot;</span></div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &quot;psvm/timer.h&quot;</span></div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="preprocessor">#include &quot;psvm/util.h&quot;</span></div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">#include &quot;psvm/io.h&quot;</span></div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="preprocessor">#include &quot;psvm/parallel_interface.h&quot;</span></div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="keyword">namespace </span>psvm {</div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div>
<div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="classpsvm_1_1_g_p_predictor.html">   40</a></span>&#160;<span class="keyword">class </span><a class="code" href="classpsvm_1_1_g_p_predictor.html">GPPredictor</a> {</div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  <span class="keywordtype">void</span> <a class="code" href="classpsvm_1_1_g_p_predictor.html#a401b72ee3267f091714ace9e0973bf01">ModelPredict</a>( <span class="keywordtype">string</span> &amp; train_file, <span class="keywordtype">string</span>&amp; test_file, <span class="keywordtype">double</span> rank_ratio );</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;</div>
<div class="line"><a name="l00059"></a><span class="lineno"><a class="line" href="classpsvm_1_1_g_p_predictor.html#a7141bfbe2731cf1ebcf7579da171b9a4">   59</a></span>&#160;  <span class="keywordtype">void</span> <a class="code" href="classpsvm_1_1_g_p_predictor.html#a7141bfbe2731cf1ebcf7579da171b9a4">GetMean</a>( <span class="keywordtype">double</span> * val, <span class="keywordtype">int</span> len ) { </div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;      <span class="keywordflow">for</span>( <span class="keywordtype">int</span> i = 0; i &lt; len; i++ ) {</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;          val[i] = (*mean_)[i][0];</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;      }</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  }</div>
<div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="classpsvm_1_1_g_p_predictor.html#a4c6662484395256ef10a337112022222">   70</a></span>&#160;  <span class="keywordtype">void</span> <a class="code" href="classpsvm_1_1_g_p_predictor.html#a4c6662484395256ef10a337112022222">GetVariance</a>( <span class="keywordtype">double</span> * val, <span class="keywordtype">int</span> len ) {</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;      <span class="keywordflow">for</span>( <span class="keywordtype">int</span> i = 0; i &lt; len; i++ ) {</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;          val[i] = (*variance_)[0][i];</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;      }</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;  }</div>
<div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="classpsvm_1_1_g_p_predictor.html#a344dac46576dee127f0bf8e67cf53314">   81</a></span>&#160;  <span class="keywordtype">void</span> <a class="code" href="classpsvm_1_1_g_p_predictor.html#a344dac46576dee127f0bf8e67cf53314">GetTrueValue</a>( <span class="keywordtype">double</span> * val, <span class="keywordtype">int</span> len ) {</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;      <span class="keywordflow">for</span>( <span class="keywordtype">int</span> i = 0; i &lt; len; i++ ) {</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;          val[i] = test_label_[i];</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;      }</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  }</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  <span class="keywordtype">void</span> <a class="code" href="classpsvm_1_1_g_p_predictor.html#ae6d9145653cef7c2c396a6a00b4d00f4">SaveResult</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* file_name);</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;</div>
<div class="line"><a name="l00103"></a><span class="lineno"><a class="line" href="classpsvm_1_1_g_p_predictor.html#ad4ead64375a1da166962e28810d7673e">  103</a></span>&#160;  <span class="keywordtype">double</span> <a class="code" href="classpsvm_1_1_g_p_predictor.html#ad4ead64375a1da166962e28810d7673e">GetRunTime</a>(){</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;      <span class="keywordflow">return</span> time_total_;</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;  }</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;  <span class="keywordtype">void</span> SetRunTime( <span class="keywordtype">double</span> time_ ){</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;      time_total_ = time_;</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;  }</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  <span class="keyword">explicit</span> GPPredictor(): test_label_(NULL), cf_(NULL), icf_(NULL),mean_(NULL), variance_(NULL){</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;  }</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;  <span class="keyword">explicit</span> GPPredictor( <span class="keywordtype">string</span> &amp; hp_file ): test_label_(NULL),mean_(NULL), variance_(NULL),cf_(NULL),icf_(NULL){</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;      cov_ = <span class="keyword">new</span> CovSEISO( hp_file );</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;  }</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;  ~GPPredictor() {</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      train_doc_.Destroy();</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;      test_doc_.Destroy();</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;      <span class="keyword">delete</span> [] test_label_;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;      <span class="keywordflow">if</span>( cf_ != NULL ){</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;          <span class="keyword">delete</span> cf_;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;      }</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;      <span class="keywordflow">if</span>( icf_ != NULL ){</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;          <span class="keyword">delete</span> icf_;</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;      }</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;      <span class="keywordflow">if</span>( mean_ != NULL ){</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;          <span class="keyword">delete</span> mean_;</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;      }</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;      <span class="keywordflow">if</span>( variance_ != NULL ){</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;          <span class="keyword">delete</span> variance_;</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;      }</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;      <span class="keywordflow">if</span>( cov_ != NULL ){</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;          <span class="keyword">delete</span> cov_;</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;      }</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;  }</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  <span class="keywordtype">void</span> GetICFRes( <span class="keyword">const</span> Document&amp; doc, <span class="keyword">const</span> GPParameter&amp; p, <span class="keyword">const</span> COV&amp; cov );</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;  <span class="keywordtype">void</span> Predict(<span class="keyword">const</span> <span class="keywordtype">char</span>* train_file, <span class="keyword">const</span> <span class="keywordtype">char</span>* test_file );</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;  <span class="keywordtype">void</span> ReadDocument(<span class="keyword">const</span> <span class="keywordtype">char</span>* train_file, <span class="keyword">const</span> <span class="keywordtype">char</span>* test_file);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;  <span class="keywordtype">void</span> PredictMean(NormalMatrix * &amp;result);</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;  <span class="keywordtype">void</span> PredictVariance(NormalMatrix *&amp;result);</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;  std::string PrintTimeInfo();</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;  <span class="comment">// Save time statistic information into a file. For processor #, it is stored in &quot;path/file_name.#&quot;</span></div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;  <span class="keywordtype">void</span> SaveTimeInfo(<span class="keyword">const</span> <span class="keywordtype">char</span> *path, <span class="keyword">const</span> <span class="keywordtype">char</span>* file_name);</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;  <span class="comment">// Stores model information including kernel info and support vectors.</span></div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;  Document train_doc_; </div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;  Document test_doc_;  </div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;  <span class="keywordtype">double</span> * test_label_;    </div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;  COV * cov_;       </div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;  LLMatrix* cf_;   </div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;  ParallelMatrix * icf_; </div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;  NormalMatrix * mean_; </div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;  NormalMatrix * variance_; </div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;  <span class="keywordtype">double</span> time_total_; </div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;};</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;}</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="ttc" id="classpsvm_1_1_g_p_predictor_html_a4c6662484395256ef10a337112022222"><div class="ttname"><a href="classpsvm_1_1_g_p_predictor.html#a4c6662484395256ef10a337112022222">psvm::GPPredictor::GetVariance</a></div><div class="ttdeci">void GetVariance(double *val, int len)</div><div class="ttdoc">This function return the variance to a vector of double, user need to allocate space for the vector a...</div><div class="ttdef"><b>Definition:</b> pgpr_picf_predict.h:70</div></div>
<div class="ttc" id="classpsvm_1_1_g_p_predictor_html_a7141bfbe2731cf1ebcf7579da171b9a4"><div class="ttname"><a href="classpsvm_1_1_g_p_predictor.html#a7141bfbe2731cf1ebcf7579da171b9a4">psvm::GPPredictor::GetMean</a></div><div class="ttdeci">void GetMean(double *val, int len)</div><div class="ttdoc">This function return the mean to a vector of double, user need to allocate space for the vector and d...</div><div class="ttdef"><b>Definition:</b> pgpr_picf_predict.h:59</div></div>
<div class="ttc" id="classpsvm_1_1_g_p_predictor_html_ad4ead64375a1da166962e28810d7673e"><div class="ttname"><a href="classpsvm_1_1_g_p_predictor.html#ad4ead64375a1da166962e28810d7673e">psvm::GPPredictor::GetRunTime</a></div><div class="ttdeci">double GetRunTime()</div><div class="ttdoc">Return the running time. </div><div class="ttdef"><b>Definition:</b> pgpr_picf_predict.h:103</div></div>
<div class="ttc" id="classpsvm_1_1_g_p_predictor_html"><div class="ttname"><a href="classpsvm_1_1_g_p_predictor.html">psvm::GPPredictor</a></div><div class="ttdoc">Predict the labels of test cases with parallel ICF Gaussian Process. </div><div class="ttdef"><b>Definition:</b> pgpr_picf_predict.h:40</div></div>
<div class="ttc" id="classpsvm_1_1_g_p_predictor_html_a344dac46576dee127f0bf8e67cf53314"><div class="ttname"><a href="classpsvm_1_1_g_p_predictor.html#a344dac46576dee127f0bf8e67cf53314">psvm::GPPredictor::GetTrueValue</a></div><div class="ttdeci">void GetTrueValue(double *val, int len)</div><div class="ttdoc">This function returns the true values of test cases to a vector of double, user need to allocate spac...</div><div class="ttdef"><b>Definition:</b> pgpr_picf_predict.h:81</div></div>
<div class="ttc" id="classpsvm_1_1_g_p_predictor_html_a401b72ee3267f091714ace9e0973bf01"><div class="ttname"><a href="classpsvm_1_1_g_p_predictor.html#a401b72ee3267f091714ace9e0973bf01">psvm::GPPredictor::ModelPredict</a></div><div class="ttdeci">void ModelPredict(string &amp;train_file, string &amp;test_file, double rank_ratio)</div><div class="ttdoc">This function prodvides the final interface to use the train file to predict all cases in test file...</div><div class="ttdef"><b>Definition:</b> pgpr_picf_predict.cc:5</div></div>
<div class="ttc" id="classpsvm_1_1_g_p_predictor_html_ae6d9145653cef7c2c396a6a00b4d00f4"><div class="ttname"><a href="classpsvm_1_1_g_p_predictor.html#ae6d9145653cef7c2c396a6a00b4d00f4">psvm::GPPredictor::SaveResult</a></div><div class="ttdeci">void SaveResult(const char *file_name)</div><div class="ttdoc">This function save the time, true values, predicted means, variance into one file. </div><div class="ttdef"><b>Definition:</b> pgpr_picf_predict.cc:152</div></div>
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